Forging a photograph is probably as old as the art of photography itself. There exist forensic methods for exposing forgeries of analog pictures. However, digital photography and powerful software to edit an image make it very easy, even for a non-specialist, to create a believable forgery of a digital photograph. As digital photography continues to replace analog, there is an urgent need to detect reliably whether a digital image has been doctored. Verifying the content of a digital image or identifying a forged segment would be useful, for example in a court of law when a digital photograph is presented as evidence.
Several different methods for detecting digital forgeries have been proposed. T. T. Ng and S. H. Chang proposed a method for detection of photomontages (“Blind Detection of Digital Photomontages using Higher Order Statistics”, ADVENT Technical Report #201-2004-1, Columbia University, June 2004). A. C. Popescu and H. Farid: developed several methods for identifying digital forgeries by tracing artifacts introduced by resampling (“Exposing Digital Forgeries by Detecting Traces of Resampling”, 53 IEEE Transactions on Signal Processing, February 2005) and Color Filter Array (CFA) interpolation (“Exposing Digital Forgeries in Color Filter Array Interpolated Images”, IEEE Transactions on Signal Processing, 2005 (in press)). Recently, M. K. Johnson and H. Farid proposed another method based on inspecting inconsistencies in lighting conditions (“Exposing Digital Forgeries by Detecting Inconsistencies in Lighting”, Proc. ACM Multimedia and Security Workshop, New York, 2005.). J. Fridrich, D. Soukal, and J. Luká{hacek over (s)} (“Detection of Copy-Move Forgery in Digital Images”, Proc. Digital Forensic Research Workshop, Cleveland, Ohio, August 2003.) established a method for detecting copy-move forgeries; a similar method was later proposed by Popescu and Farid (“Exposing Digital Forgeries by Detecting Duplicated Image Regions”, Technical Report, TR2004-515, Dartmouth College, Computer Science 2004.).
For each of these methods, there are circumstances when they will fail to detect a forgery. Ng's and Chang's photomontages detection method, for instance, has very restrictive assumptions that are usually not fulfilled. Even when they are, the method has a misclassification rate of about 28% (“Blind Detection of Digital Photomontages using Higher Order Statistics”, ADVENT Technical Report #201-2004-1, Columbia University, June 2004, page 34). The method of detecting copy-move forgery is limited to one particular kind of forgery, where a certain part of an image is copied and pasted somewhere else in the same image (e.g., to cover an object). Methods based on detecting traces of resampling may produce less reliable results for processed images stored in JPEG format. The method based on detection of inconsistencies in lighting assumes nearly Lambertian surfaces for both the forged and the original areas. It might fail to work when the object does not have a compatible surface, when pictures of both the original and forged objects were taken under similar lighting conditions, or during a cloudy day when no directional light source is present. In particular, none of these prior-art methods uses the pattern noise of the imaging sensor. Thus there is a need for apparatus and method that overcome the drawbacks of the prior art in detecting digital forgeries.